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Unveiling the Root Cause of EV Charging Irregularities: A Statistical Approach

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Artificial Intelligence for Sustainable Energy (GENCITY 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1142))

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Abstract

The drive for mobility has caused an increase in greenhouse gas emissions, leading to a shift toward the adoption of electric vehicles (EVs). These vehicles are powered by efficient electric motors, which offer reduced upkeep and enhanced operation. However, the unstable nature of electricity demand and the proliferation of EV charging equipment providers have posed significant challenges in EV charging. Furthermore, charging interruptions are commonplace, with electrical vehicle supply equipment (EVSE) serving as a mysterious black box for EV owners. By analyzing EVSE and EV data during charging, sensitive signals of charging termination can be discovered. The present research paper aims to investigate endurance data to uncover the root cause of EV charging interruption and to address uncertainties in EV charging.

Supported by Mercedes-Benz Research and Development India

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Acknowledgements

This work has been supported by Mercedes-Benz Research and Development India and Mercedes-Benz AG. We express our appreciation to Mercedes-Benz for furnishing the resources and infrastructure necessary to finalize this research. We accept that any opinions expressed in this paper are solely those of the authors.

Author information

Authors and Affiliations

  1. Department of Computer Science Engineering, Malaviya National Institute of Technology, Jaipur, India

    Ankit Bajaj & Dinesh Gopalani

  2. Mercedes-Benz Research and Development, Bengaluru, India

    Rachit Mathur, Hemanjaneya Reddy, Swapna Satyanarayan & Ansuman Chand

Authors
  1. Ankit Bajaj
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  2. Dinesh Gopalani
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  3. Rachit Mathur
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  4. Hemanjaneya Reddy
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  5. Swapna Satyanarayan
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  6. Ansuman Chand
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Corresponding author

Correspondence to Ankit Bajaj .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, Bihar, India

    Jimson Mathew

  2. Department of Electrical and Electronic Engineering, University of Southampton Malaysia, Iskandar Puteri, Johor, Malaysia

    Lenin Gopal

  3. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China

    Filbert H. Juwono

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Bajaj, A., Gopalani, D., Mathur, R., Reddy, H., Satyanarayan, S., Chand, A. (2024). Unveiling the Root Cause of EV Charging Irregularities: A Statistical Approach. In: Mathew, J., Gopal, L., Juwono, F.H. (eds) Artificial Intelligence for Sustainable Energy. GENCITY 2023. Lecture Notes in Electrical Engineering, vol 1142. Springer, Singapore. https://doi.org/10.1007/978-981-99-9833-3_7

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  • DOI: https://doi.org/10.1007/978-981-99-9833-3_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9832-6

  • Online ISBN: 978-981-99-9833-3

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